For all the experiments, the inference noise schedules (\(\beta _0, .., \beta _T\) ) were defined as described in the Wavegrad paper [1]}. For 1000 and 100 iterations the noise schedule is linear, for 25 iterations it comes from the Fibonacci and for 6 iterations we performed a model-dependent grid search to find the best noise schedule parameters. For other hyper-parameters (e.g. learning rate, batch size, etc) we use the same as in Wavegrad [1]}. Training was performed using the following form of Eq. REF , e.g. \(\theta _t = \sqrt{\bar{\alpha }_t} \theta _0\)  and \(k_t = \dfrac{\beta _t}{\bar{\alpha }_t{\theta _0}^2}\) . Our best results were obtained using \(\theta _0 = 0.001\) .
